SOTAVerified

Federated Learning

Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.

This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.

Papers

Showing 17011750 of 6771 papers

TitleStatusHype
Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions0
Fed-ZOE: Communication-Efficient Over-the-Air Federated Learning via Zeroth-Order Estimation0
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation0
AutoRank: MCDA Based Rank Personalization for LoRA-Enabled Distributed Learning0
DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game0
The Impact of Cut Layer Selection in Split Federated Learning0
Summary of Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images0
LoLaFL: Low-Latency Federated Learning via Forward-only Propagation0
FLAMe: Federated Learning with Attention Mechanism using Spatio-Temporal Keypoint Transformers for Pedestrian Fall Detection in Smart Cities0
FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning0
Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration0
Robust Federated Learning in the Face of Covariate Shift: A Magnitude Pruning with Hybrid Regularization Framework for Enhanced Model Aggregation0
FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning0
Federated Unlearning Model Recovery in Data with Skewed Label Distributions0
On the Robustness of Distributed Machine Learning against Transfer AttacksCode0
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data0
Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models0
Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence0
Federated t-SNE and UMAP for Distributed Data Visualization0
Covariances for Free: Exploiting Mean Distributions for Federated Learning with Pre-Trained ModelsCode0
SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated LearningCode0
Building Gradient Bridges: Label Leakage from Restricted Gradient Sharing in Federated Learning0
Concurrent vertical and horizontal federated learning with fuzzy cognitive maps0
Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning0
FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated LearningCode0
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training0
TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning0
Vertical Federated Unlearning via Backdoor CertificationCode0
Information-Geometric Barycenters for Bayesian Federated Learning0
Just a Simple Transformation is Enough for Data Protection in Vertical Federated LearningCode0
Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning0
UA-PDFL: A Personalized Approach for Decentralized Federated Learning0
Modeling Inter-Intra Heterogeneity for Graph Federated LearningCode0
F-RBA: A Federated Learning-based Framework for Risk-based Authentication0
ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes0
Predicting Survival of Hemodialysis Patients using Federated Learning0
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and RegressionCode0
Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks0
Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series DataCode0
ExclaveFL: Providing Transparency to Federated Learning using Exclaves0
Client-Side Patching against Backdoor Attacks in Federated Learning0
Deep Learning Model Security: Threats and Defenses0
Predicting Quality of Video Gaming Experience Using Global-Scale Telemetry Data and Federated Learning0
Federated In-Context LLM Agent Learning0
dsLassoCov: a federated machine learning approach incorporating covariate control0
How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?0
Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph GenerationCode0
Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation0
A Tutorial of Personalized Federated Recommender Systems: Recent Advances and Future Directions0
Learn How to Query from Unlabeled Data Streams in Federated LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75Unverified
2SiloBN + SAMmIoU49.1Unverified
3SiloBNmIoU45.96Unverified
4FedSAM + SWAmIoU43.42Unverified
5FedASAM + SWAmIoU43.02Unverified
6FedAvg + SWAmIoU42.48Unverified
7FedASAMmIoU42.27Unverified
8FedSAMmIoU41.22Unverified
9FedAvgmIoU38.65Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32Unverified
2FedSAM + SWAAcc@1-1262Clients68.12Unverified
3FedAvg + SWAAcc@1-1262Clients67.52Unverified
4FedASAMAcc@1-1262Clients64.23Unverified
5FedSAMAcc@1-1262Clients63.72Unverified
6FedAvgAcc@1-1262Clients61.91Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64Unverified
2FedASAMACC@1-100Clients39.76Unverified
3FedSAM + SWAACC@1-100Clients39.51Unverified
4FedSAMACC@1-100Clients36.93Unverified
5FedAvgACC@1-100Clients36.74Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62Unverified
2FedASAMACC@1-100Clients40.81Unverified
3FedSAM + SWAACC@1-100Clients39.24Unverified
4FedAvgACC@1-100Clients38.59Unverified
5FedSAMACC@1-100Clients38.56Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72Unverified
2FedSAM + SWAACC@1-100Clients46.76Unverified
3FedASAMACC@1-100Clients46.58Unverified
4FedSAMACC@1-100Clients44.84Unverified
5FedAvgACC@1-100Clients41.27Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27Unverified
2FedASAMACC@1-100Clients47.78Unverified
3FedSAM + SWAACC@1-100Clients46.47Unverified
4FedSAMACC@1-100Clients46.05Unverified
5FedAvgACC@1-100Clients42.17Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17Unverified
2FedSAM + SWAACC@1-100Clients47.96Unverified
3FedASAMACC@1-100Clients45.61Unverified
4FedSAMACC@1-100Clients44.73Unverified
5FedAvgACC@1-100Clients40.43Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01Unverified
2FedSAM + SWAACC@1-100Clients39.3Unverified
3FedASAMACC@1-100Clients36.04Unverified
4FedSAMACC@1-100Clients31.04Unverified
5FedAvgACC@1-100Clients30.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97Unverified
2FedASAM + SWAACC@1-100Clients54.79Unverified
3FedSAM + SWAACC@1-100Clients53.67Unverified
4FedSAMACC@1-100Clients53.39Unverified
5FedAvgACC@1-100Clients50.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5Unverified
2FedSAM + SWAACC@1-100Clients54.36Unverified
3FedASAM + SWAACC@1-100Clients54.1Unverified
4FedSAMACC@1-100Clients53.97Unverified
5FedAvgACC@1-100Clients50.66Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81Unverified
2FedSAMACC@1-100Clients54.01Unverified
3FedSAM + SWAACC@1-100Clients53.9Unverified
4FedASAM + SWAACC@1-100Clients53.86Unverified
5FedAvgACC@1-100Clients49.92Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2Unverified